Bài tập Biến hình và Xử lý ảnh
¶

Họ và Tên: Huỳnh Nguyễn Thế Dân

MSSV: 21110256

Lớp: 21TTH1

Yêu cầu 1 : Thực hiện tốt hơn việc segmentation bàn tay với các phần xương, da, và background bằng các thuật toán global và local threshoding như trong file hướng dẫn thực hành¶

(Gợi ý : kiểm tra xem các kênh màu khác hoặc kiểm tra việc áp dụng các chức năng canh chỉnh histogram (histogram equalization) hay gamma correction)

In [ ]:
import os

path = os.getcwd()
os.chdir(path)
print(os.listdir())

['21110256_HuynhNguyenTheDan_ IS Lab01.html', '21110256_HuynhNguyenTheDan_ IS Lab01.ipynb', '21110256_HuynhNguyenTheDan_ IS Lab01.pdf', '21110259_LeQuocDat_IS lab01.pdf', 'Object Segmentation Data']
In [ ]:
os.chdir(os.path.join(path, 'Object Segmentation Data'))
print(os.listdir())

['Activities.jpeg', 'Barcode.png', 'Bone.jpg', 'Brain.jpg', 'Car.jpg', 'Chest.jpg', 'Cloths.jpg', 'Code.jpg', 'Crack.jpg', 'Cross.jpg', 'Defect.jpg', 'Dust.jpg', 'Emotion.jpg', 'Face.jpg', 'Fire.jpg', 'Gesture.jpg', 'Hand.jpg', 'Iris.jpg', 'Leaf.jpg', 'Lung.png', 'Mask.jpg', 'Melanoma.jpg', 'QR.jpg', 'Retina.jpg', 'Shelf.jpg', 'Sign.jpg', 'Tumor.png', 'Writing.png']
In [ ]:
import numpy as np 
import cv2 
from matplotlib import pyplot as plt 
from skimage.color import rgb2gray
from skimage.filters import threshold_otsu
from skimage.measure import label, regionprops
from skimage.segmentation import mark_boundaries
from scipy import ndimage as ndi
import pandas as pd
import json
import os
import timeit
import random
In [ ]:
def ShowImage(ImageList, nRows = 1, nCols = 2, WidthSpace = 0.00, HeightSpace = 0.00):
    from matplotlib import pyplot as plt 
    import matplotlib.gridspec as gridspec
    
    gs = gridspec.GridSpec(nRows, nCols)     
    gs.update(wspace=WidthSpace, hspace=HeightSpace) # set the spacing between axes.
    plt.figure(figsize=(20,20))
    for i in range(len(ImageList)):
        ax1 = plt.subplot(gs[i])
        ax1.set_xticklabels([])
        ax1.set_yticklabels([])
        ax1.set_aspect('equal')

        plt.subplot(nRows, nCols,i+1)

        image = ImageList[i].copy()
        if (len(image.shape) < 3):
            plt.imshow(image, plt.cm.gray)
        else:
            plt.imshow(image)
        plt.title("Image " + str(i))
        plt.axis('off')

    plt.show()
In [ ]:
import os
import pandas as pd

def get_subfiles(dir):
    "Get a list of immediate subfiles"
    return next(os.walk(dir))[2]
In [ ]:
path_Data = os.path.join(path, 'Object Segmentation Data')
os.listdir(path_Data)
Out[ ]:
['Activities.jpeg',
 'Barcode.png',
 'Bone.jpg',
 'Brain.jpg',
 'Car.jpg',
 'Chest.jpg',
 'Cloths.jpg',
 'Code.jpg',
 'Crack.jpg',
 'Cross.jpg',
 'Defect.jpg',
 'Dust.jpg',
 'Emotion.jpg',
 'Face.jpg',
 'Fire.jpg',
 'Gesture.jpg',
 'Hand.jpg',
 'Iris.jpg',
 'Leaf.jpg',
 'Lung.png',
 'Mask.jpg',
 'Melanoma.jpg',
 'QR.jpg',
 'Retina.jpg',
 'Shelf.jpg',
 'Sign.jpg',
 'Tumor.png',
 'Writing.png']
In [ ]:
path_Data = os.path.join(path, 'Object Segmentation Data')
all_names = sorted(get_subfiles(path_Data))
print("Number of Images:", len(all_names))
IMG = [] 
for i in range(len(all_names)): 
  tmp = cv2.imread(os.path.join(path_Data,all_names[i]))
  IMG.append(tmp)
    
ImageDB = IMG.copy()
NameDB = all_names
print(NameDB)

Number of Images: 28
['Activities.jpeg', 'Barcode.png', 'Bone.jpg', 'Brain.jpg', 'Car.jpg', 'Chest.jpg', 'Cloths.jpg', 'Code.jpg', 'Crack.jpg', 'Cross.jpg', 'Defect.jpg', 'Dust.jpg', 'Emotion.jpg', 'Face.jpg', 'Fire.jpg', 'Gesture.jpg', 'Hand.jpg', 'Iris.jpg', 'Leaf.jpg', 'Lung.png', 'Mask.jpg', 'Melanoma.jpg', 'QR.jpg', 'Retina.jpg', 'Shelf.jpg', 'Sign.jpg', 'Tumor.png', 'Writing.png']

Segmentation phần da của tay¶

In [ ]:
FileName = 'Hand.jpg'
idx = NameDB.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", NameDB[idx])

image_orig = ImageDB[idx]
image_gray = cv2.cvtColor(image_orig,cv2.COLOR_BGR2GRAY)
image_hsv = cv2.cvtColor(image_orig, cv2.COLOR_BGR2HSV)
image_ycbcr = cv2.cvtColor(image_orig, cv2.COLOR_BGR2YCR_CB)
ShowImage([image_orig, image_gray, image_hsv, image_ycbcr], 1, 4)

Selected Image :  
Index  16 
Name  Hand.jpg
No description has been provided for this image
In [ ]:
def p_tile_threshold(image, pct):
  n_pixels = pct * image.shape[0] * image.shape[1]
  hist = np.histogram(image, bins=range(256))[0]
  hist = np.cumsum(hist)

  return np.argmin(np.abs(hist - n_pixels))
In [ ]:
T = p_tile_threshold(image_gray, pct = 0.55)
print(T)

mask_obj = image_gray > T
mask_bg = image_gray <= T
ShowImage([image_gray, mask_obj, mask_bg], 1, 3)

96
No description has been provided for this image
In [ ]:
def otsu(gray):
  pixel_number = gray.shape[0] * gray.shape[1]
  mean_weigth = 1.0/pixel_number
  his, bins = np.histogram(gray, np.array(range(0, 256)))
  final_thresh = -1
  final_value = -1

  WBackground = []
  WForeground = []
  Values = []
  
  for t in bins[1:-1]: # This goes from 1 to 254 uint8 range (Pretty sure wont be those values)
      Wb = np.sum(his[:t]) * mean_weigth
      Wf = np.sum(his[t:]) * mean_weigth

      mub = np.mean(his[:t])
      muf = np.mean(his[t:])

      value = Wb * Wf * (mub - muf) ** 2
      # print("Wb", Wb, "Wf", Wf)
      # print("t", t, "value", value)
      WBackground.append(Wb)
      WForeground.append(Wf)
      Values.append(value)

      if value > final_value:
          final_thresh = t
          final_value = value

  final_img = gray.copy()
  print(final_thresh)
  final_img[gray > final_thresh] = 255
  final_img[gray < final_thresh] = 0
  return final_img, final_thresh, [WBackground, WForeground, Values]
In [ ]:
final_img, final_thresh, parms = otsu(image_gray)
ShowImage([image_gray, final_img], 1, 2)

97
No description has been provided for this image
In [ ]:
WBackground, WForeground, Values = parms[0], parms[1], parms[2]
print(WBackground)
print(WForeground)
print(Values)

plt.plot(Values)
plt.show()
plt.plot(WBackground)
plt.plot(WForeground)
plt.show()

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No description has been provided for this image
No description has been provided for this image

Segmentation với phần xương của tay¶

In [ ]:
def two_peaks_threshold(image, smooth_hist=True, sigma=5):
    from scipy.ndimage import gaussian_filter
    
    hist = np.histogram(image, bins=range(256))[0].astype(np.float64)
    plt.plot(hist)
    plt.show()

    if smooth_hist:
        hist = gaussian_filter(hist, sigma=sigma)
        plt.plot(hist)
        plt.show()

    f_peak = np.argmax(hist)

    # finding second peak
    s_peak = np.argmax((np.arange(len(hist)) - f_peak) ** 2 * hist)

    thr = np.argmin(hist[min(f_peak, s_peak): max(f_peak, s_peak)])
    thr += min(f_peak, s_peak)

    final_img = image.copy()
    print(thr)
    final_img[image > thr] = 255
    final_img[image < thr] = 0

    return final_img, thr, hist
In [ ]:
final_img, final_thresh, hist = two_peaks_threshold(image_gray)
ShowImage([image_gray, final_img], 1, 2)
No description has been provided for this image
No description has been provided for this image 
134
No description has been provided for this image

Multi-Otsu (Segmentation với background của phần tay)¶

In [ ]:
from skimage.filters import threshold_multiotsu
# Applying multi-Otsu threshold for the default value, generating
# three classes.
thresholds = threshold_multiotsu(image_gray)
# Using the threshold values, we generate the three regions.
regions = np.digitize(image_gray, bins=thresholds)

fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(20, 7))
# Plotting the original image.
ax[0].imshow(image_gray, cmap='gray')
ax[0].set_title('Original')
ax[0].axis('off')
# Plotting the histogram and the two thresholds obtained from
# multi-Otsu.
ax[1].hist(image_gray.ravel(), bins=255)
ax[1].set_title('Histogram')
for thresh in thresholds:
    ax[1].axvline(thresh, color='r')
# Plotting the Multi Otsu result.
ax[2].imshow(regions, cmap='jet')
ax[2].set_title('Multi-Otsu result')
ax[2].axis('off')
plt.subplots_adjust()
plt.show()
No description has been provided for this image
In [ ]:
Segments = []
for idx in list(np.unique(regions)):
  mask = regions == idx
  Segments.append(mask)

ShowImage(Segments, 1, len(Segments))
No description has been provided for this image
In [ ]:
thresholds = threshold_multiotsu(image_gray, classes=5)
regions = np.digitize(image_gray, bins=thresholds)

fig, ax = plt.subplots(nrows=1, ncols=3, figsize=(20, 7))
# Plotting the original image.
ax[0].imshow(image_gray, cmap='gray')
ax[0].set_title('Original')
ax[0].axis('off')
# Plotting the histogram and the two thresholds obtained from
# multi-Otsu.
ax[1].hist(image_gray.ravel(), bins=255)
ax[1].set_title('Histogram')
for thresh in thresholds:
    ax[1].axvline(thresh, color='r')
# Plotting the Multi Otsu result.
ax[2].imshow(regions, cmap='jet')
ax[2].set_title('Multi-Otsu result')
ax[2].axis('off')
plt.subplots_adjust()
plt.show()
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Yêu cầu 2 : Chọn thêm 2 ví dụ trong danh sách hình và định nghĩa object cần segment trong các hình là gì và thực hiện segmentation tốt nhất bằng global và local thresholdin¶

In [ ]:
import numpy as np
import cv2
from matplotlib import pyplot as plt
from pylab import imread
from skimage.color import rgb2gray
from skimage.filters import threshold_otsu
from skimage.measure import label, regionprops
from skimage.segmentation import mark_boundaries
from scipy import ndimage as ndi
import pandas as pd
import json
import os
import timeit
import random

Segment object Chest¶

In [ ]:
FileName = 'Chest.jpg'
idx = NameDB.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", NameDB[idx])

image_orig = ImageDB[idx]
ShowImage([image_orig], 1)

Selected Image :  
Index  5 
Name  Chest.jpg
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In [ ]:
def p_tile_threshold(image, pct):
  n_pixels = pct * image.shape[0] * image.shape[1]
  hist = np.histogram(image, bins=range(256))[0]
  hist = np.cumsum(hist)
  return np.argmin(np.abs(hist - n_pixels))
In [ ]:
image_color = image_orig.copy()
image_gray = cv2.cvtColor(image_color, cv2.COLOR_RGB2GRAY)

T = p_tile_threshold(image_gray, pct = 0.40)
print(T)

mask_obj = image_gray > T
mask_bg = image_gray <= T
ShowImage([image_gray, mask_obj, mask_bg], 1, 3, WidthSpace = 0.10, HeightSpace = 0.10)

138
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Segment object People¶

In [ ]:
FileName = 'Cloths.jpg'
idx = NameDB.index(FileName)
print("Selected Image : ", "\nIndex ", idx, "\nName ", NameDB[idx])

image_orig = ImageDB[idx]
ShowImage([image_orig], 1)

Selected Image :  
Index  6 
Name  Cloths.jpg
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In [ ]:
def p_tile_threshold(image_orig, pct):
    n_pixels = pct * image_orig.shape[0] * image_orig.shape[1]
    hist = np.histogram(image_orig, bins=range(256))[0]
    hist = np.cumsum(hist)
    
    return np.argmin(np.abs(hist - n_pixels))
In [ ]:
image_color = image_orig.copy()
image_gray = cv2.cvtColor(image_color, cv2.COLOR_RGB2GRAY)
T = p_tile_threshold(image_gray, pct = 0.35)
print(T)
mask_obj = image_gray > T
mask_bg = image_gray <= T
ShowImage([image_gray, mask_obj, mask_bg], 1, 3, WidthSpace = 0.10, HeightSpace = 0.10)

155
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